Handheld electronic device and method for learning contextual data during disambiguation of text input

ABSTRACT

A handheld electronic device includes a reduced QWERTY keyboard and is enabled with disambiguation software that is operable to disambiguate text input. In addition to identifying and outputting representations of language objects that are stored in the memory and that correspond with a text input, the device is able to employ contextual data in certain circumstances to prioritize output and to learn new contextual data.

BACKGROUND

1. Field

The disclosed and claimed concept relates generally to handheldelectronic devices and, more particularly, to a handheld electronicdevice having a reduced keyboard and a text input disambiguationfunction that can employ contextual data.

2. Background Information

Numerous types of handheld electronic devices are known. Examples ofsuch handheld electronic devices include, for instance, personal dataassistants (PDAs), handheld computers, two-way pagers, cellulartelephones, and the like. Many handheld electronic devices also featurewireless communication capability, although many such handheldelectronic devices are stand-alone devices that are functional withoutcommunication with other devices.

Such handheld electronic devices are generally intended to be portable,and thus are of a relatively compact configuration in which keys andother input structures often perform multiple functions under certaincircumstances or may otherwise have multiple aspects or featuresassigned thereto. With advances in technology, handheld electronicdevices are built to have progressively smaller form factors yet haveprogressively greater numbers of applications and features residentthereon. As a practical matter, the keys of a keypad can only be reducedto a certain small size before the keys become relatively unusable. Inorder to enable text entry, however, a keypad must be capable ofentering all twenty-six letters of the Latin alphabet, for instance, aswell as appropriate punctuation and other symbols.

One way of providing numerous letters in a small space has been toprovide a “reduced keyboard” in which multiple letters, symbols, and/ordigits, and the like, are assigned to any given key. For example, atouch-tone telephone includes a reduced keypad by providing twelve keys,of which ten have digits thereon, and of these ten keys eight have Latinletters assigned thereto. For instance, one of the keys includes thedigit “2” as well as the letters “A”, “B”, and “C”. Other known reducedkeyboards have included other arrangements of keys, letters, symbols,digits, and the like. Since a single actuation of such a key potentiallycould be intended by the user to refer to any of the letters “A”, “B”,and “C”, and potentially could also be intended to refer to the digit“2”, the input generally is an ambiguous input and is in need of sometype of disambiguation in order to be useful for text entry purposes.

In order to enable a user to make use of the multiple letters, digits,and the like on any given key, numerous keystroke interpretation systemshave been provided. For instance, a “multi-tap” system allows a user tosubstantially unambiguously specify a particular character on a key bypressing the same key a number of times equivalent to the position ofthe desired character on the key. Another exemplary keystrokeinterpretation system would include key chording, of which various typesexist. For instance, a particular character can be entered by pressingtwo keys in succession or by pressing and holding first key whilepressing a second key. Still another exemplary keystroke interpretationsystem would be a “press-and-hold/press-and-release” interpretationfunction in which a given key provides a first result if the key ispressed and immediately released, and provides a second result if thekey is pressed and held for a short period of time. Another keystrokeinterpretation system that has been employed is a software-based textdisambiguation function. In such a system, a user typically presses keysto which one or more characters have been assigned, generally pressingeach key one time for each desired letter, and the disambiguationsoftware attempt to predict the intended input. Numerous such systemshave been proposed, and while many have been generally effective fortheir intended purposes, shortcomings still exist.

It would be desirable to provide an improved handheld electronic devicewith a reduced keyboard that seeks to mimic a QWERTY keyboard experienceor other particular keyboard experience. Such an improved handheldelectronic device might also desirably be configured with enoughfeatures to enable text entry and other tasks with relative ease.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed and claimed concept can be gainedfrom the following Description when read in conjunction with theaccompanying drawings in which:

FIG. 1 is a top plan view of an improved handheld electronic device inaccordance with the disclosed and claimed concept;

FIG. 2 is a schematic depiction of the improved handheld electronicdevice of FIG. 1;

FIG. 2A is a schematic depiction of a portion of the handheld electronicdevice of FIG. 2;

FIGS. 3A, 3B, and 3C are an exemplary flowchart depicting certainaspects of a disambiguation function that can be executed on thehandheld electronic device of FIG. 1;

FIG. 4 is another exemplary flowchart depicting certain aspects of alearning method that can be executed on the handheld electronic device;

FIG. 5 is an exemplary output during a text entry operation;

FIG. 6 is another exemplary output during another part of the text entryoperation;

FIG. 7 is another exemplary output during another part of the text entryoperation;

FIG. 8 is another exemplary output during another part of the text entryoperation;

FIG. 9 is an exemplary flowchart depicting the use of context dataduring a text entry operation.

Similar numerals refer to similar parts throughout the specification.

DESCRIPTION

An improved handheld electronic device 4 is indicated generally in FIG.1 and is depicted schematically in FIG. 2. The exemplary handheldelectronic device 4 includes a housing 6 upon which are disposed aprocessor unit that includes an input apparatus 8, an output apparatus12, a processor 16, a memory 20, and at least a first routine. Theprocessor 16 may be, for instance, and without limitation, amicroprocessor (μP) and is responsive to inputs from the input apparatus8 and provides output signals to the output apparatus 12. The processor16 also interfaces with the memory 20. The processor 16 and the memory20 together form a processor apparatus. Examples of handheld electronicdevices are included in U.S. Pat. Nos. 6,452,588 and 6,489,950, whichare incorporated by record herein.

As can be understood from FIG. 1, the input apparatus 8 includes akeypad 24 and a thumbwheel 32. As will be described in greater detailbelow, the keypad 24 is in the exemplary form of a reduced QWERTYkeyboard including a plurality of keys 28 that serve as input members.It is noted, however, that the keypad 24 may be of other configurations,such as an AZERTY keyboard, a QWERTZ keyboard, or other keyboardarrangement, whether presently known or unknown, and either reduced ornot reduced. As employed herein, the expression “reduced” and variationsthereof in the context of a keyboard, a keypad, or other arrangement ofinput members, shall refer broadly to an arrangement in which at leastone of the input members has assigned thereto a plurality of linguisticelements such as, for example, characters in the set of Latin letters,whereby an actuation of the at least one of the input members, withoutanother input in combination therewith, is an ambiguous input since itcould refer to more than one of the plurality of linguistic elementsassigned thereto. As employed herein, the expression “linguisticelement” and variations thereof shall refer broadly to any element thatitself can be a language object or from which a language object can beconstructed, identified, or otherwise obtained, and thus would include,for example and without limitation, characters, letters, strokes,ideograms, phonemes, morphemes, digits, and the like. As employedherein, the expression “language object” and variations thereof shallrefer broadly to any type of object that may be constructed, identified,or otherwise obtained from one or more linguistic elements, that can beused alone or in combination to generate text, and that would include,for example and without limitation, words, shortcuts, symbols,ideograms, and the like.

The system architecture of the handheld electronic device 4advantageously is organized to be operable independent of the specificlayout of the keypad 24. Accordingly, the system architecture of thehandheld electronic device 4 can be employed in conjunction withvirtually any keypad layout substantially without requiring anymeaningful change in the system architecture. It is further noted thatcertain of the features set forth herein are usable on either or both ofa reduced keyboard and a non-reduced keyboard.

The keys 28 are disposed on a front face of the housing 6, and thethumbwheel 32 is disposed at a side of the housing 6. The thumbwheel 32can serve as another input member and is both rotatable, as is indicatedby the arrow 34, to provide selection inputs to the processor 16, andalso can be pressed in a direction generally toward the housing 6, as isindicated by the arrow 38, to provide another selection input to theprocessor 16.

As can further be seen in FIG. 1, many of the keys 28 include a numberof linguistic elements 48 disposed thereon. As employed herein, theexpression “a number of” and variations thereof shall refer broadly toany quantity, including a quantity of one. In the exemplary depiction ofthe keypad 24, many of the keys 28 include two linguistic elements, suchas including a first linguistic element 52 and a second linguisticelement 56 assigned thereto.

One of the keys 28 of the keypad 24 includes as the characters 48thereof the letters “Q” and “W”, and an adjacent key 28 includes as thecharacters 48 thereof the letters “E” and “R”. It can be seen that thearrangement of the characters 48 on the keys 28 of the keypad 24 isgenerally of a QWERTY arrangement, albeit with many of the keys 28including two of the characters 48.

The output apparatus 12 includes a display 60 upon which can be providedan output 64. An exemplary output 64 is depicted on the display 60 inFIG. 1. The output 64 includes a text component 68 and a variantcomponent 72. The variant component 72 includes a default portion 76 anda variant portion 80. The display also includes a caret 84 that depictsgenerally where the next input from the input apparatus 8 will bereceived.

The text component 68 of the output 64 provides a depiction of thedefault portion 76 of the output 64 at a location on the display 60where the text is being input. The variant component 72 is disposedgenerally in the vicinity of the text component 68 and provides, inaddition to the default proposed output 76, a depiction of the variousalternate text choices, i.e., alternates to the default proposed output76, that are proposed by an input disambiguation function in response toan input sequence of key actuations of the keys 28.

As will be described in greater detail below, the default portion 76 isproposed by the disambiguation function as being the most likelydisambiguated interpretation of the ambiguous input provided by theuser. The variant portion 80 includes a predetermined quantity ofalternate proposed interpretations of the same ambiguous input fromwhich the user can select, if desired. It is noted that the exemplaryvariant portion 80 is depicted herein as extending vertically below thedefault portion 76, but it is understood that numerous otherarrangements could be provided.

The memory 20 is depicted schematically in FIG. 2A. The memory 20 can beany of a variety of types of internal and/or external storage media suchas, without limitation, RAM, ROM, EPROM(s), EEPROM(s), and the like thatprovide a storage register for data storage such as in the fashion of aninternal storage area of a computer, and can be volatile memory ornonvolatile memory. The memory 20 additionally includes a number ofroutines depicted generally with the numeral 22 for the processing ofdata. The routines 22 can be in any of a variety of forms such as,without limitation, software, firmware, and the like. As will beexplained in greater detail below, the routines 22 include theaforementioned disambiguation function as an application, as well asother routines.

As can be understood from FIG. 2A, the memory 20 additionally includesdata stored and/or organized in a number of tables, sets, lists, and/orotherwise. Specifically, the memory 20 includes a generic word list 88,a new words database 92, another data source 99 and a contextual datatable 49.

Stored within the various areas of the memory 20 are a number oflanguage objects 100 and frequency objects 104. The language objects 100generally are each associated with an associated frequency object 104.The language objects 100 include, in the present exemplary embodiment, aplurality of word objects 108 and a plurality of N-gram objects 112. Theword objects 108 are generally representative of complete words withinthe language or custom words stored in the memory 20. For instance, ifthe language stored in the memory 20 is, for example, English, generallyeach word object 108 would represent a word in the English language orwould represent a custom word.

Associated with substantially each word object 108 is a frequency object104 having frequency value that is indicative of the relative frequencywithin the relevant language of the given word represented by the wordobject 108. In this regard, the generic word list 88 includes aplurality of word objects 108 and associated frequency objects 104 thattogether are representative of a wide variety of words and theirrelative frequency within a given vernacular of, for instance, a givenlanguage. The generic word list 88 can be derived in any of a widevariety of fashions, such as by analyzing numerous texts and otherlanguage sources to determine the various words within the languagesources as well as their relative probabilities, i.e., relativefrequencies, of occurrences of the various words within the languagesources.

The N-gram objects 112 stored within the generic word list 88 are shortstrings of characters within the relevant language typically, forexample, one to three characters in length, and typically represent wordfragments within the relevant language, although certain of the N-gramobjects 112 additionally can themselves be words. However, to the extentthat an N-gram object 112 also is a word within the relevant language,the same word likely would be separately stored as a word object 108within the generic word list 88. As employed herein, the expression“string” and variations thereof shall refer broadly to an object havingone or more characters or components, and can refer to any of a completeword, a fragment of a word, a custom word or expression, and the like.

In the present exemplary embodiment of the handheld electronic device 4,the N-gram objects 112 include 1-gram objects, i.e., string objects thatare one character in length, 2-gram objects, i.e., string objects thatare two characters in length, and 3-gram objects, i.e., string objectsthat are three characters in length, all of which are collectivelyreferred to as N-grams 112. Substantially each N-gram object 112 in thegeneric word list 88 is similarly associated with an associatedfrequency object 104 stored within the generic word list 88, but thefrequency object 104 associated with a given N-gram object 112 has afrequency value that indicates the relative probability that thecharacter string represented by the particular N-gram object 112 existsat any location within any word of the relevant language. The N-gramobjects 112 and the associated frequency objects 104 are a part of thecorpus of the generic word list 88 and are obtained in a fashion similarto the way in which the word object 108 and the associated frequencyobjects 104 are obtained, although the analysis performed in obtainingthe N-gram objects 112 will be slightly different because it willinvolve analysis of the various character strings within the variouswords instead of relying primarily on the relative occurrence of a givenword.

The present exemplary embodiment of the handheld electronic device 4,with its exemplary language being the English language, includestwenty-six 1-gram N-gram objects 112, i.e., one 1-gram object for eachof the twenty-six letters in the Latin alphabet upon which the Englishlanguage is based, and further includes 676 2-gram N-gram objects 112,i.e., twenty-six squared, representing each two-letter permutation ofthe twenty-six letters within the Latin alphabet.

The N-gram objects 112 also include a certain quantity of 3-gram N-gramobjects 112, primarily those that have a relatively high frequencywithin the relevant language. The exemplary embodiment of the handheldelectronic device 4 includes fewer than all of the three-letterpermutations of the twenty-six letters of the Latin alphabet due toconsiderations of data storage size, and also because the 2-gram N-gramobjects 112 can already provide a meaningful amount of informationregarding the relevant language. As will be set forth in greater detailbelow, the N-gram objects 112 and their associated frequency objects 104provide frequency data that can be attributed to character strings forwhich a corresponding word object 108 cannot be identified or has notbeen identified, and typically is employed as a fallback data source,although this need not be exclusively the case.

In the present exemplary embodiment, the language objects 100 and thefrequency objects 104 are maintained substantially inviolate in thegeneric word list 88, meaning that the basic language dictionary remainssubstantially unaltered within the generic word list 88, and thelearning functions that are provided by the handheld electronic device 4and that are described below operate in conjunction with other objectthat are generally stored elsewhere in memory 20, such as, for example,in the new words database 92.

The new words database 92 stores additional word objects 108 andassociated frequency objects 104 in order to provide to a user acustomized experience in which words and the like that are usedrelatively more frequently by a user will be associated with relativelyhigher frequency values than might otherwise be reflected in the genericword list 88. More particularly, the new words database 92 includes wordobjects 108 that are user-defined and that generally are not found amongthe word objects 108 of the generic word list 88. Each word object 108in the new words database 92 has associated therewith an associatedfrequency object 104 that is also stored in the new words database 92.

FIGS. 3A, 3B, and 3C depict in an exemplary fashion the generaloperation of certain aspects of the disambiguation function of thehandheld electronic device 4. Additional features, functions, and thelike are depicted and described elsewhere.

An input is detected, as at 204, and the input can be any type ofactuation or other operation as to any portion of the input apparatus 8.A typical input would include, for instance, an actuation of a key 28having a number of characters 48 thereon, or any other type of actuationor manipulation of the input apparatus 8.

The disambiguation function then determines, as at 212, whether thecurrent input is an operational input, such as a selection input, adelimiter input, a movement input, an alternation input, or, forinstance, any other input that does not constitute an actuation of a key28 having a number of characters 48 thereon. If the input is determinedat 212 to not be an operational input, processing continues at 216 byadding the input to the current input sequence which may or may notalready include an input.

Many of the inputs detected at 204 are employed in generating inputsequences as to which the disambiguation function will be executed. Aninput sequence is build up in each “session” with each actuation of akey 28 having a number of characters 48 thereon. Since an input sequencetypically will be made up of at least one actuation of a key 28 having aplurality of characters 48 thereon, the input sequence will beambiguous. When a word, for example, is completed the current session isended an a new session is initiated.

An input sequence is gradually built up on the handheld electronicdevice 4 with each successive actuation of a key 28 during any givensession. Specifically, once a delimiter input is detected during anygiven session, the session is terminated and a new session is initiated.Each input resulting from an actuation of one of the keys 28 having anumber of the characters 48 associated therewith is sequentially addedto the current input sequence. As the input sequence grows during agiven session, the disambiguation function generally is executed witheach actuation of a key 28, i.e., input, and as to the entire inputsequence. Stated otherwise, within a given session, the growing inputsequence is attempted to be disambiguated as a unit by thedisambiguation function with each successive actuation of the variouskeys 28.

Once a current input representing a most recent actuation of the one ofthe keys 28 having a number of the characters 48 assigned thereto hasbeen added to the current input sequence within the current session, asat 216 in FIG. 3A, the disambiguation function generates, as at 220,substantially all of the permutations of the characters 48 assigned tothe various keys 28 that were actuated in generating the input sequence.In this regard, the “permutations” refer to the various strings that canresult from the characters 48 of each actuated key 28 limited by theorder in which the keys 28 were actuated. The various permutations ofthe characters in the input sequence are employed as prefix objects.

For instance, if the current input sequence within the current sessionis the ambiguous input of the keys “AS” and “OP”, the variouspermutations of the first character 52 and the second character 56 ofeach of the two keys 28, when considered-in the sequence in which thekeys 28 were actuated, would be “SO”, “SP”, “AP”, and “AO”, and each ofthese is a prefix object that is generated, as at 220, with respect tothe current input sequence. As will be explained in greater detailbelow, the disambiguation function seeks to identify for each prefixobject one of the word objects 108 for which the prefix object would bea prefix.

For each generated prefix object, the memory 20 is consulted, as at 224,to identify, if possible, for each prefix object one of the word objects108 in the memory 20 that corresponds with the prefix object, meaningthat the sequence of letters represented by the prefix object would beeither a prefix of the identified word object 108 or would besubstantially identical to the entirety of the word object 108. Furtherin this regard, the word object 108 that is sought to be identified isthe highest frequency word object 108. That is, the disambiguationfunction seeks to identify the word object 108 that corresponds with theprefix object and that also is associated with a frequency object 104having a relatively higher frequency value than any of the otherfrequency objects 104 associated with the other word objects 108 thatcorrespond with the prefix object.

It is noted in this regard that the word objects 108 in the generic wordlist 88 are generally organized in data tables that correspond with thefirst two letters of various words. For instance, the data tableassociated with the prefix “CO” would include all of the words such as“CODE”, “COIN”, “COMMUNICATION”, and the like. Depending upon thequantity of word objects 108 within any given data table, the data tablemay additionally include sub-data tables within which word objects 108are organized by prefixes that are three characters or more in length.Continuing onward with the foregoing example, if the “CO” data tableincluded, for instance, more than 256 word objects 108, the “CO” datatable would additionally include one or more sub-data tables of wordobjects 108 corresponding with the most frequently appearingthree-letter prefixes. By way of example, therefore, the “CO” data tablemay also include a “COM” sub-data table and a “CON” sub-data table. If asub-data table includes more than the predetermined number of wordobjects 108, for example a quantity of 256, the sub-data table mayinclude further sub-data tables, such as might be organized according toa four letter prefixes. It is noted that the aforementioned quantity of256 of the word objects 108 corresponds with the greatest numericalvalue that can be stored within one byte of the memory 20.

Accordingly, when, at 224, each prefix object is sought to be used toidentify a corresponding word object 108, and for instance the instantprefix object is “AP”, the “AP” data table will be consulted. Since allof the word objects 108 in the “AP” data table will correspond with theprefix object “AP”, the word object 108 in the “AP” data table withwhich is associated a frequency object 104 having a frequency valuerelatively higher than any of the other frequency objects 104 in the“AP” data table is identified. The identified word object 108 and theassociated frequency object 104 are then stored in a result registerthat serves as a result of the various comparisons of the generatedprefix objects with the contents of the memory 20.

It is noted that one or more, or possibly all, of the prefix objectswill be prefix objects for which a corresponding word object 108 is notidentified in the memory 20. Such prefix objects are considered to beorphan prefix objects and are separately stored or are otherwiseretained for possible future use. In this regard, it is noted that manyor all of the prefix objects can become orphan object if, for instance,the user is trying to enter a new word or, for example, if the user hasmis-keyed and no word corresponds with the mis-keyed input.

Processing continues, as at 232, where duplicate word objects 108associated with relatively lower frequency values are deleted from theresult. Such a duplicate word object 108 could be generated, forinstance, by the other data source 99.

Once the duplicate word objects 108 and the associated frequency objects104 have been removed at 232, processing branches, as at 234, to asubsystem in FIG. 9, described below, wherein the need to examinecontext data is evaluated. Once context data is evaluated, as in FIG. 9,processing returns to 236, as in FIG. 3C, wherein the remaining prefixobjects are arranged in an output set in decreasing order of frequencyvalue.

If it is determined, as at 240, that the flag has been set, meaning thata user has made a selection input, either through an express selectioninput or through an alternation input of a movement input, then thedefault output 76 is considered to be “locked,” meaning that theselected variant will be the default prefix until the end of thesession. If it is determined at 240 that the flag has been set, theprocessing will proceed to 244 where the contents of the output set willbe altered, if needed, to provide as the default output 76 an outputthat includes the selected prefix object, whether it corresponds with aword object 108 or is an artificial variant. In this regard, it isunderstood that the flag can be set additional times during a session,in which case the selected prefix associated with resetting of the flagthereafter becomes the “locked” default output 76 until the end of thesession or until another selection input is detected.

Processing then continues, as at 248, to an output step after which anoutput 64 is generated as described above. Processing thereaftercontinues at 204 where additional input is detected. On the other hand,if it is determined at 240 that the flag had not been set, thenprocessing goes directly to 248 without the alteration of the contentsof the output set at 244.

If the detected input is determined, as at 212, to be an operationalinput, processing then continues to determine the specific nature of theoperational input. For instance, if it is determined, as at 252, thatthe current input is a selection input, processing continues at 254where the flag is set. Processing then returns to detection ofadditional inputs as at 204.

If it is determined, as at 260, that the input is a delimiter input,processing continues at 264 where the current session is terminated andprocessing is transferred, as at 266, to the learning functionsubsystem, as at 404 of FIG. 4. A delimiter input would include, forexample, the actuation of a <SPACE> key 116, which would both enter adelimiter symbol and would add a space at the end of the word, actuationof the <ENTER> key, which might similarly enter a delimiter input andenter a space, and by a translation of the thumbwheel 32, such as isindicated by the arrow 38, which might enter a delimiter input withoutadditionally entering a space.

It is first determined, as at 408, whether the default output at thetime of the detection of the delimiter input at 260 matches a wordobject 108 in the memory 20. If it does not, this means that the defaultoutput is a user-created output that should be added to the new wordsdatabase 92 for future use. In such a circumstance processing thenproceeds to 412 where the default output is stored in the new wordsdatabase 92 as a new word object 108. Additionally, a frequency object104 is stored in the new words database 92 and is associated with theaforementioned new word object 108. The new frequency object 104 isgiven a relatively high frequency value, typically within the upperone-fourth or one-third of a predetermined range of possible frequencyvalues.

In this regard, frequency objects 104 are given an absolute frequencyvalue generally in the range of zero to 65,535. The maximum valuerepresents the largest number that can be stored within two bytes of thememory 20. The new frequency object 104 that is stored in the new wordsdatabase 92 is assigned an absolute frequency value within the upperone-fourth or one-third of this range, particularly since the new wordwas used by a user and is likely to be used again.

With further regard to frequency object 104, it is noted that within agiven data table, such as the “CO” data table mentioned above, theabsolute frequency value is stored only for the frequency object 104having the highest frequency value within the data table. All of theother frequency objects 104 in the same data table have frequency valuesstored as percentage values normalized to the aforementioned maximumabsolute frequency value. That is, after identification of the frequencyobject 104 having the highest frequency value within a given data table,all of the other frequency objects 104 in the same data table areassigned a percentage of the absolute maximum value, which representsthe ratio of the relatively smaller absolute frequency value of aparticular frequency object 104 to the absolute frequency value of theaforementioned highest value frequency object 104. Advantageously, suchpercentage values can be stored within a single byte of memory, thussaving storage space within the handheld electronic device 4.

Upon creation of the new word object 108 and the new frequency object104, and storage thereof within the new words database 92, processing istransferred to 420 where the learning process is terminated. Processingis then returned to the main process, as at 204. If at 408 it isdetermined that the word object 108 in the default output 76 matches aword object 108 within the memory 20, processing is returned directly tothe main process at 204.

With further regard to the identification of various word objects 108for correspondence with generated prefix objects, it is noted that thememory 20 can include a number of additional data sources 99 in additionto the generic word list 88 and the new words database 92, all of whichcan be considered linguistic sources. It is understood that the memory20 might include any number of other data sources 99. The other datasources 99 might include, for example, an address database, a speed-textdatabase, or any other data source without limitation. An exemplaryspeed-text database might include, for example, sets of words orexpressions or other data that are each associated with, for example, acharacter string that may be abbreviated. For example, a speed-textdatabase might associate the string “br” with the set of words “BestRegards”, with the intention that a user can type the string “br” andreceive the output “Best Regards”.

In seeking to identify word objects 108 that correspond with a givenprefix object, the handheld electronic device 4 may poll all of the datasources in the memory 20. For instance the handheld electronic device 4may poll the generic word list 88, the new words database 92, and theother data sources 99 to identify word objects 108 that correspond withthe prefix object. The contents of the other data sources 99 may betreated as word objects 108, and the processor 16 may generate frequencyobjects 104 that will be associated with such word objects 108 and towhich may be assigned a frequency value in, for example, the upperone-third or one-fourth of the aforementioned frequency range. Assumingthat the assigned frequency value is sufficiently high, the string “br”,for example, would typically be output to the display 60. If a delimiterinput is detected with respect to the portion of the output having theassociation with the word object 108 in the speed-text database, forinstance “br”, the user would receive the output “Best Regards”, itbeing understood that the user might also have entered a selection inputas to the exemplary string “br”.

The contents of any of the other data sources 99 may be treated as wordobjects 108 and may be associated with generated frequency objects 104having the assigned frequency value in the aforementioned upper portionof the frequency range. After such word objects 108 are identified, thenew word learning function can, if appropriate, act upon such wordobjects 108 in the fashion set forth above.

If it is determined, such as at 268, that the current input is amovement input, such as would be employed when a user is seeking to editan object, either a completed word or a prefix object within the currentsession, the caret 84 is moved, as at 272, to the desired location, andthe flag is set, as at 276. Processing then returns to where additionalinputs can be detected, as at 204.

In this regard, it is understood that various types of movement inputscan be detected from the input device 8. For instance, a rotation of thethumbwheel 32, such as is indicated by the arrow 34 of FIG. 1, couldprovide a movement input. In the instance where such a movement input isdetected, such as in the circumstance of an editing input, the movementinput is additionally detected as a selection input. Accordingly, and asis the case with a selection input such as is detected at 252, theselected variant is effectively locked with respect to the defaultportion 76 of the output 64. Any default output 76 during the samesession will necessarily include the previously selected variant.

In the present exemplary embodiment of the handheld electronic device 4,if it is determined, as at 252, that the input is not a selection input,and it is determined, as at 260, that the input is not a delimiterinput, and it is further determined, as at 268, that the input is not amovement input, in the current exemplary embodiment of the handheldelectronic device 4 the only remaining operational input generally is adetection of the <DELETE> key 86 of the keys 28 of the keypad 24. Upondetection of the <DELETE> key 86, the final character of the defaultoutput is deleted, as at 280. Processing thereafter returns to 204 whereadditional input can be detected.

An exemplary input sequence is depicted in FIGS. 1 and 5-8. In thisexample, the user is attempting to enter the word “APPLOADER”, and thisword presently is not stored in the memory 20. In FIG. 1 the user hasalready typed the “AS” key 28. Since the data tables in the memory 20are organized according to two-letter prefixes, the contents of theoutput 64 upon the first keystroke are obtained from the N-gram objects112 within the memory. The first keystroke “AS” corresponds with a firstN-gram object 112 “S” and an associated frequency object 104, as well asanother N-gram object 112 “A” and an associated frequency object 104.While the frequency object 104 associated with “S” has a frequency valuegreater than that of the frequency object 104 associated with “A”, it isnoted that “A” is itself a complete word. A complete word is alwaysprovided as the default output 76 in favor of other prefix objects thatdo not match complete words, regardless of associated frequency value.As such, in FIG. 1, the default portion 76 of the output 64 is “A”.

In FIG. 5, the user has additionally entered the “OP” key 28. Thevariants are depicted in FIG. 5. Since the prefix object “SO” is also aword, it is provided as the default output 76. In FIG. 6, the user hasagain entered the “OP” key 28 and has also entered the “L” key 28. It isnoted that the exemplary “L” key 28 depicted herein includes only thesingle character 48 “L”.

It is assumed in the instant example that no operational inputs havethus far been detected. The default output 76 is “APPL”, such as wouldcorrespond with the word “APPLE”. The prefix “APPL” is depicted both inthe text component 68, as well as in the default portion 76 of thevariant component 72. Variant prefix objects in the variant portion 80include “APOL”, such as would correspond with the word “APOLOGIZE”, andthe prefix “SPOL”, such as would correspond with the word “SPOLIATION”.

It is particularly noted that the additional variants “AOOL”, “AOPL”,“SOPL”, and “SOOL” are also depicted as variants 80 in the variantcomponent 72. Since no word object 108 corresponds with these prefixobjects, the prefix objects are considered to be orphan prefix objectsfor which a corresponding word object 108 was not identified. In thisregard, it may be desirable for the variant component 72 to include aspecific quantity of entries, and in the case of the instant exemplaryembodiment the quantity is seven entries. Upon obtaining the result at224, if the quantity of prefix objects in the result is fewer than thepredetermined quantity, the disambiguation function will seek to provideadditional outputs until the predetermined number of outputs areprovided.

In FIG. 7 the user has additionally entered the “OP” key 28. In thiscircumstance, and as can be seen in FIG. 7, the default portion 76 ofthe output 64 has become the prefix object “APOLO” such as wouldcorrespond with the word “APOLOGIZE”, whereas immediately prior to thecurrent input the default portion 76 of the output 64 of FIG. 6 was“APPL” such as would correspond with the word “APPLE.” Again, assumingthat no operational inputs had been detected, the default prefix objectin FIG. 7 does not correspond with the previous default prefix object ofFIG. 6. As such, a first artificial variant “APOLP” is generated and inthe current example is given a preferred position. The aforementionedartificial variant “APOLP” is generated by deleting the final characterof the default prefix object “APOLO” and by supplying in its place anopposite character 48 of the key 28 which generated the final characterof the default portion 76 of the output 64, which in the current exampleof FIG. 7 is “P”, so that the aforementioned artificial variant is“APOLP”.

Furthermore, since the previous default output “APPL” corresponded witha word object 108, such as the word object 108 corresponding with theword “APPLE”, and since with the addition of the current input theprevious default output “APPL” no longer corresponds with a word object108, two additional artificial variants are generated. One artificialvariant is “APPLP” and the other artificial variant is “APPLO”, andthese correspond with the previous default output “APPL” plus thecharacters 48 of the key 28 that was actuated to generate the currentinput. These artificial variants are similarly output as part of thevariant portion 80 of the output 64.

As can be seen in FIG. 7, the default portion 76 of the output 64“APOLO” no longer seems to match what would be needed as a prefix for“APPLOADER”, and the user likely anticipates that the desired word“APPLOADER” is not already stored in the memory 20. As such, the userprovides a selection input, such as by scrolling with the thumbwheel 32until the variant string “APPLO” is highlighted. The user then continuestyping and enters the “AS” key.

The output 64 of such action is depicted in FIG. 8. Here, the string“APPLOA” is the default portion 76 of the output 64. Since the variantstring “APPLO” became the default portion 76 of the output 64 (notexpressly depicted herein) as a result of the selection input as to thevariant string “APPLO”, and since the variant string “APPLO” does notcorrespond with a word object 108, the character strings “APPLOA” and“APPLOS” were created as an artificial variants. Additionally, since theprevious default of FIG. 7, “APOLO” previously had corresponded with aword object 108, but now is no longer in correspondence with the defaultportion 76 of the output 64 of FIG. 8, the additional artificialvariants of “APOLOA” and “APOLOS” were also generated. Such artificialvariants are given a preferred position in favor of the three displayedorphan prefix objects.

Since the current input sequence in the example no longer correspondswith any word object 108, the portions of the method related toattempting to find corresponding word objects 108 are not executed withfurther inputs for the current session. That is, since no word object108 corresponds with the current input sequence, further inputs willlikewise not correspond with any word object 108. Avoiding the search ofthe memory 20 for such nonexistent word objects 108 saves time andavoids wasted processing effort.

As the user continues to type, the user ultimately will successfullyenter the word “APPLOADER” and will enter a delimiter input. Upondetection of the delimiter input after the entry of “APPLOADER”, thelearning function is initiated. Since the word “APPLOADER” does notcorrespond with a word object 108 in the memory 20, a new word object108 corresponding with “APPLOADER” is generated and is stored in the newwords database 92, along with a corresponding new frequency object 104which is given an absolute frequency in the upper, say, one-third orone-fourth of the possible frequency range. In this regard, it is notedthat the new words database 92 is generally organized in two-characterprefix data tables similar to those found in the generic word list 88.As such, the new frequency object 104 is initially assigned an absolutefrequency value, but upon storage the absolute frequency value, if it isnot the maximum value within that data table, will be changed to includea normalized frequency value percentage normalized to whatever is themaximum frequency value within that data table.

It is noted that the layout of the characters 48 disposed on the keys 28in FIG. 1 is an exemplary character layout that would be employed wherethe intended primary language used on the handheld electronic device 4was, for instance, English. Other layouts involving these characters 48and/or other characters can be used depending upon the intended primarylanguage and any language bias in the makeup of the language objects100.

As mentioned elsewhere herein, a complete word that is identified duringa disambiguation cycle is always provided as a default output 76 infavor of other prefix objects that do not match complete words,regardless of associated frequency value. That is, a word object 108corresponding with an ambiguous input and having a length equal to thatof the ambiguous input is output at a position of priority over otherprefix objects. As employed herein, the expression “length” andvariations thereof shall refer broadly to a quantity of elements ofwhich an object is comprised, such as the quantity of linguisticelements of which a language object 100 is comprised.

If more than one complete word is identified during a disambiguationcycle, all of the complete words may be output in order of decreasingfrequency with respect to one another, with each being at a position ofpriority over the prefix objects that are representative of incompletewords. However, it may be desirable in certain circumstances to employadditional data, if available, to prioritize the complete words in a waymore advantageous to the user.

The handheld electronic device 4 thus advantageously includes thecontextual data table 49 stored in the memory 20. The exemplarycontextual data table 49 can be said to have stored therein a number ofambiguous words and associated context data.

Specifically, the contextual data table 49 comprises a number of keyobjects 47 and, associated with each key object 47, a number ofassociated contextual value objects 51. In the present exemplaryembodiment in which the English language is employed on the handheldelectronic device 4, each key object 47 is a word object 108. That is, akey object 47 in the contextual data table 49 is also stored as a wordobject 108 in one of the generic word list 88, the new words database92, and the other data sources 99. Each key object 47 has associatedtherewith one or more contextual value objects 51 that are eachrepresentative of a particular contextual data element. If a key object47 is identified during a cycle of disambiguation with respect to anambiguous input, and if a contextual value object 51 associated with thekey object 47 coincides with a context of the ambiguous input, the wordobject 108 corresponding with the key object 47 is output as a defaultword output at the text component 68 and at the default portion 76 ofthe variant component 72 In other embodiments, however, it is understoodthat the key objects 47 could be in forms other than in the form of wordobjects 108.

The contents of the contextual data table 49 are obtained by analyzingthe language objects 100 and the data corpus from which the languageobjects 100 and frequency objects 104 were obtained. First, the languageobjects 100 are analyzed to identify ambiguous word objects 108. A setof ambiguous word objects 108 are representative of a plurality ofcomplete words which are each formed from the same ambiguous input suchas, for example, the words “TOP” and “TOO”, which are each formed fromthe ambiguous input <TY> <OP> <OP>. Each ambiguous word object 108 hasassociated therewith a frequency object 104. In a given set of ambiguousword objects 108, each ambiguous word object 108 with which isassociated a frequency object 104 having a frequency value less than thehighest in the set is a candidate key object 47. That is, in a given setof ambiguous word objects 108, all of the ambiguous word objects 108 arecandidate key objects 47, except for the ambiguous word object 108having associated therewith the frequency object 104 having therelatively highest frequency value in the set. This is because, as willbe explained in greater detail elsewhere herein, the anticipatedsituation in which context data is relevant during a text entry iswherein a plurality of ambiguous word objects 108 are identified in adisambiguation cycle, and a lower-frequency ambiguous word object 108 isdesirably output at a relatively preferred position because it would bea more appropriate solution in a particular context.

Once the candidate key objects 47 are identified, the data corpus isanalyzed to identify any valid contextual data for the candidate keyobjects 47. Valid contextual data is any particular context whereinoccurs any statistically significant incidence of a particular keyobject 47.

One exemplary context is that in which a particular ambiguous wordfollows, to a statistically significant extent, a particular word. Forinstance, and continuing the example above, it may be determined thatthe key word “TOP” occurs, to a statistically significant extent, afterthe context word “TABLE” and after the context word “HILL”. Dependingupon the configuration of the contextual data table 49, such a contextmight be limited to a particular word that immediately precedes aparticular ambiguous word, or it might include a particular word thatprecedes a particular ambiguous word by one, two, three, or more words.That is, the ambiguous key word “TOP” might occur to a statisticallysignificant extent when it immediately follows the context word “TABLE”,but the same ambiguous key word “TOP” might occur to a statisticallysignificant extent when it follows the context word “HILL” immediatelyor by two, three, or four words. In such a circumstance, the ambiguousword object 108 “TOP” would be stored as a key object 47, and the wordobjects 108 “TABLE” and “HILL” would be stored as two associatedcontextual value objects 51.

Another exemplary context is that in which a particular ambiguous wordis, to a statistically significant extent, a first word in a sentence.In such a situation, the identified context might be that in which theparticular ambiguous word follows, to a statistically significantextent, one or more particular punctuation marks such as the period “.”,the question mark “?”, and the exclamation point “!”. In such asituation, the contextual value object 51 would be the particularpunctuation symbol, with each such statistically significant punctuationsymbol being a separate contextual value object 51.

Still another exemplary context is that in which a particular ambiguousword follows, to a statistically significant extent, another entry thatis in a predetermined format. In such a situation, the identifiedcontext might, for example, be that in which the particular ambiguousword follows, to a statistically significant extent, an entry that has apredetermined arrangement of numeric components. For instance, anumerically indicated date might be indicated in any of the followingformats: NN/NN/NNNN or NN/NN/NN or N/NN/NNNN or N/NN/NN or N/N/NNNN orN/N/NN or other formats, wherein “N” refers to an Arabic digit, and “/”might refer to any of a particular symbol, a delimiter, or a “null” suchas a <SPACE> or nothing. As such, it might be determined that aparticular ambiguous word follows, to a statistically significantextent, another entry that is in any of one or more of the formatsNN/NN/NNNN or NN/NN/NN or N/NN/NNNN or N/NN/NN or N/N/NNNN or N/N/NN.Again, the particular ambiguous word might immediately follow theformatted entry or might follow two, three, or more words behind theformatted entry. In such a situation, the contextual value object 51would be a representation of the particular format, with each suchstatistically significant format being a separate contextual valueobject 51.

More specifically, the contextual value objects 51 can each be stored asa hash, i.e., a integer value that results from a mathematicalmanipulation. For instance, the two contextual value objects 51 “TABLE”and “HILL”, while being word objects 108, would be stored in thecontextual data table 49 as hashes of the words “TABLE” and “HILL”. Thekey objects 47, such as the word “TOP” can similarly each be stored as ahash.

The three contextual value objects 51 “.”, “?”, and “!” would each bestored as a hash, i.e., an integer value, that would be more in thenature of a flag, i.e., an integer value representative of a punctuationsymbol itself or being of a value that is different than the hash of anyof the twenty-six Latin letters. The contextual value objects 51 in thenature of predetermined formats could be similarly stored.

During text entry, the disambiguation system maintains in a temporarymemory register a hash of a number of the entries preceding the currentambiguous input. For instance, if the user is attempting to enter thephrase, “CLIMB THE HILL AND REACH THE TOP”, the disambiguation routine22 would have calculated and stored a hash of each of one or more of thewords “CLIMB”, “THE”, “HILL”, “AND”, “REACH”, and “THE” as entry values53 prior to the user entering the series of keystrokes <TY> <OP> <OP>,which would result in the ambiguous words “TOO” and “TOP”. The memory 20may be configured to store only a predetermined quantity of such entryvalues 53, which would be replaced on a first-in-first-out basis asadditional words are entered. For instance, if the memory only storedthe last four entries as entry values 53, the four entry values inexistence at the time the user was entering the keystrokes for the word“TOP” would be hashes of the words “HILL”, “AND”, “REACH”, and “THE”. Inother systems, for example, the disambiguation routine might store as anentry value only the one entry immediately preceding the current input.

Once the user enters the series of keystrokes <TY> <OP> <OP>, thedisambiguation routine would determine that the two word objects 108“TOO” and “TOP” each correspond with and have a length equal to that ofthe series of keystrokes <TY> <OP> <OP>, and thus would determine thatthe two word objects 108 “TOO” and “TOP” represent ambiguous words. Ifit is assumed that the ambiguous word object 108 “TOO” has associatedtherewith a frequency object 104 having a frequency value higher thanthat of the frequency object 104 associated with the ambiguous wordobject 108 “TOP”, the disambiguation routine 22 will consult thecontextual data table 49 to determine whether the text already inputprovides a context wherein it would be appropriate to output the wordobject 108 “TOP” at a position of higher priority than the higherfrequency word object 108 “TOO”.

Specifically, the disambiguation routine 22 would look to see if thecontextual data table 49 has stored therein a key object 47 matching theword object 108 “TOP”. If such a key object 47 is found, the variouscontextual value objects 51 associated with the key object 47 “TOP” arecompared with each of the entry values 53 which, in the present example,would be hashes of the words “HILL”, “AND”, “REACH”, and “THE” todetermine whether or not any of the contextual value objects 51 coincidewith any of the entry values 53. As employed herein, the expression“coincide” and variations thereof shall refer broadly to any type ofpredetermined equivalence, correspondence, association, and the like,the existence of which can be ascertained between two or more objects.Since one of the contextual value objects 51 associated with the keyobject 47 “TOP” is a hash of the word object 108 “HILL”, and since oneof the entry values 53 is a hash of the previously entered word “HILL”,upon comparison the two hashes will be found to coincide on the basis ofbeing equal. As a result, the key object 47, i.e., the ambiguous wordobject 108, “TOP” will be output at a position of priority with respectto the ambiguous word object 108 “TOO” despite the ambiguous word object108 “TOO” being of a relatively higher frequency.

The disambiguation routine 22 would also store as entry values 53 hashesrepresentative of punctuation symbols and non-word entries for use incomparison with contextual value objects 51 in the same fashion. This isuseful when searching for contexts wherein the contextual value objects51 are representative of punctuation marks, predetermined formats, andthe like. For instance, if the predetermined format is a date formatsuch as suggested above, an associated key object 47 will be output at apreferred position if it is preceded by an entry in the form of a date.It is understood that numerous other types of predefined formats couldbe employed, such as other date format like “Month date, year” or “dateMonth year”, time formats, and any other type of predetermined format ifdetermined to be a statistically significant context. It is alsounderstood that numerous other types of contexts could be identified,stored, and employed with the disambiguation routine 22 withoutdeparting from the present concept.

The present system is particularly advantageous due to its flexibility.It does not require the establishment of blanket “rules” forprioritization of words in contexts. Rather, each lesser-frequencyambiguous word has associated therewith statistically significantcontext data, which enables the handheld electronic device 4 to beadaptable and customizable to the needs of the user.

Briefly summarized, therefore, and depicted generally in FIG. 9 asbranching from the main process at 234 in FIG. 3A, the disambiguationroutine 22 determines, as at 604, whether or not at least two of theword objects 108 identified at 224 in FIG. 3A and stored in the resulteach have a length equal to that of the ambiguous input, and thus areambiguous word objects 108. If not, processing returns, as at 608, tothe main process at 236 in FIG. 3C. If it is determined at 604 that theresult includes at least two ambiguous word objects 108, processingcontinues to 612 where it is determined whether or not a key object 47corresponding with one of the ambiguous word objects 108 other than thehighest frequency word object 108 is stored in the contextual data table49. If not, processing returns, as at 608, to the main process at 236 inFIG. 3C.

If it is determined at 612 that a corresponding key object 47 exists,processing continues, as at 616, where the contextual value objects 51associated with the identified key object 47 are each compared with thestored entry values 53 to identify whether or not any key object 47 andany entry value 53 coincide. If none coincide, processing returns, as at608, to the main process at 236 in FIG. 3C. However, if it is determinedat 616 that a key object 47 and an entry value 53 coincide, then theword object 108 corresponding with the key object 47 is output, as at620, at a position of priority with respect to the highest-frequencyambiguous word object 108 identified at 604. Processing returns, as at608, to the main process at 236 in FIG. 3C.

The disambiguation routine 22 additionally is advantageously configuredto learn certain contextual data. Specifically, the disambiguationroutine 22 can identify the preceding-word type context data when a useron two separate occasions selects a particular less-preferred ambiguousword object 108 in the same context.

For instance, on a first occasion a plurality of ambiguous word objects108 may be output in response to a first ambiguous input, and a user mayselect a particular less-preferred ambiguous word object 108. In such acircumstance, the selected less-preferred ambiguous word object 108 andthe specific context are stored as an entry in a candidate data file.

If on a second occasion a plurality of ambiguous word objects 108 areoutput in response to a second ambiguous input, and if the user selectsa less-preferred ambiguous word object 108, the less-preferred ambiguousword object 108 and the context are compared with the various entries inthe candidate data file. If an entry is found in the candidate data filethat matches the less-preferred ambiguous word object 108 and thecontext of the second ambiguous input, the entry is moved from thecandidate data file to the contextual data table 49. The newly storedentry in the contextual data table 49 can thereafter be employed as setforth above.

It is noted however, that the candidate data file is a data buffer oflimited capacity. As additional entries are added to the candidate datafile, older entries which have not been moved to the contextual datatable 49 are deleted on a first-in-first-out basis. The limited size ofthe candidate data file thus adds to the contextual learning functionsomething of a frequency-of-use limitation. That is, depending uponusage, an entry in the candidate data file can either be moved to thecontextual data table 49 or can be removed from the candidate data fileto make room for additional entries. If the entry is moved to thecontextual data table 49, this would indicate that the user desired theparticular less-preferred ambiguous word object 108 in the particularcontext with sufficient frequency to warrant the saving thereof as validcontextual data. On the other hand, deletion of the candidate entry tomake room for additional candidate entries would indicate that thecandidate entry was not used with sufficient regularity or frequency towarrant its being saved as learned valid contextual data in thecontextual data table 49.

The selected less-preferred ambiguous word object 108 will be stored asa key object 47 in the contextual data table 49 if such a key object 47does not already exist. Additionally, a hash of the preceding-wordcontext is stored as a contextual value object 51 and is associated withthe aforementioned key object 47. In this regard, the preceding-wordcontext might simply be the immediately preceding word. It isunderstood, however, that the context potentially could be one wherein aparticular context word precedes by two, three, or more words theambiguous word object 108 for which the context is learned. Such contextadvantageously can be learned for word objects 108 in the new wordsdatabase 92 and in any other data source in the memory 20. It is alsounderstood that other types of contexts can be learned by thedisambiguation routine 22.

Moreover, learned contextual data can be unlearned. For instance, aparticular key object 47 and a corresponding particular contextual valueobject 51 may be added to the contextual data table 49 via theaforementioned learning function. At some point in the future the usermay begin in the particular context to prefer an output that hadpreviously been a default output, in favor of which a less-preferredambiguous word object 108 had been selected on two occasion and becamestored as context data. If this happens on two occasions, the previouslylearned particular key object 47 and corresponding particular contextualvalue object 51 are advantageously unlearned, i.e., are deleted from thecontextual data table 49. That is, the system operates as though thepreviously learned particular key object 47 and corresponding particularcontextual value object 51 were determined to not be valid contextualdata. This avoids the use of contextual data that is not desired or thatis considered to be invalid.

While specific embodiments of the disclosed and claimed concept havebeen described in detail, it will be appreciated by those skilled in theart that various modifications and alternatives to those details couldbe developed in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosed andclaimed concept which is to be given the full breadth of the claimsappended and any and all equivalents thereof.

1. A method of enabling input into a handheld electronic device of a type including an input apparatus, an output apparatus, and a processor apparatus comprising a memory having stored therein a plurality of objects including a plurality of language objects and a number of contextual values, at least some of the language objects each having associated therewith at least a first contextual value, the input apparatus including a plurality of input members, at least some of the input members each having a plurality of linguistic elements assigned thereto, the method comprising: detecting a first input; outputting as a first output an interpretations of the first input; detecting as a second input an ambiguous input that comprises a number of input member actuations; outputting at least a portion of each of a first language object and a second language object as proposed interpretations of the ambiguous input, the at least a portion of the first language object being output at a position of preference with respect to the at least a portion of the second language object; detecting a selection of the at least a portion of the second language object; detecting another first input; outputting as another first output an interpretations of the another first input, the first output and the another first output being the same; detecting as another second input another ambiguous input that comprises another number of input member actuations; outputting at least a portion of each of another first language object and another second language object as proposed interpretations of the another ambiguous input, the at least a portion of the another first language object being output at a position of preference with respect to the at least a portion of the another second language object, the second language object and the another second language object being the same; detecting a selection of the at least a portion of the another second language object and, responsive thereto: storing at least one of a representation of the another first input and a representation of the another first output as a contextual value, associating the contextual value with the another second language object.
 2. The method of claim 1, further comprising: detecting an additional first input; outputting an additional first output as an interpretations of the additional first input; detecting as an additional second input an additional ambiguous input that comprises an additional number of input member actuations; generating an output set comprising at least a portion of each of an additional first language object and an additional second language object as proposed interpretations of the additional ambiguous input, the second language object and the additional second language object being the same; making a determination that at least one of a representation of the additional first input and a representation of the additional first output coincides with the contextual value; and responsive to said making a determination, outputting the at least a portion of the additional second language object at a position of preference with respect to the at least a portion of the additional first language object.
 3. The method of claim 2, further comprising: identifying as the additional first language object a word object representative of a relatively higher-frequency ambiguous word; identifying as the additional second language object a word object representative of a relatively lower-frequency ambiguous word; and making as said determination a determination that the lower-frequency ambiguous word, in the context of the additional first output, should be output at a position of preference to the higher-frequency ambiguous word.
 4. The method of claim 2, further comprising: detecting a selection of the at least a portion of the additional first language object; and responsive to said detecting a selection, deleting the contextual value.
 5. The method of claim 1, further comprising: identifying as the first language object a language object of a length equal to that of the ambiguous input; identifying as the second language object a language object of a length equal to that of the ambiguous input; identifying as the another first language object a language object of a length equal to that of the another ambiguous input; identifying as the another second language object a language object of a length equal to that of the another ambiguous input; and responsive thereto, initiating said storing and said associating.
 6. A handheld electronic device comprising an input apparatus, a processor apparatus, and an output apparatus, the input apparatus comprising a number of input members, the processor apparatus comprising a processor and a memory having stored therein a plurality of objects comprising a plurality of language objects and a number of contextual values, at least some of the language objects each having associated therewith at least a first contextual value, the memory having stored therein a number of routines which, when executed by the processor, cause the handheld electronic device to be adapted to perform operations comprising: detecting a first input; outputting as a first output an interpretations of the first input; detecting as a second input an ambiguous input that comprises a number of input member actuations; outputting at least a portion of each of a first language object and a second language object as proposed interpretations of the ambiguous input, the at least a portion of the first language object being output at a position of preference with respect to the at least a portion of the second language object; detecting a selection of the at least a portion of the second language object; detecting another first input; outputting as another first output an interpretations of the another first input, the first output and the another first output being the same; detecting as another second input another ambiguous input that comprises another number of input member actuations; outputting at least a portion of each of another first language object and another second language object as proposed interpretations of the another ambiguous input, the at least a portion of the another first language object being output at a position of preference with respect to the at least a portion of the another second language object, the second language object and the another second language object being the same; detecting a selection of the at least a portion of the another second language object and, responsive thereto: storing at least one of a representation of the another first input and a representation of the another first output as a contextual value, associating the contextual value with the another second language object.
 7. The handheld electronic device of claim 6 wherein the operations further comprise: detecting an additional first input; outputting an additional first output as an interpretations of the additional first input; detecting as an additional second input an additional ambiguous input that comprises an additional number of input member actuations; generating an output set comprising at least a portion of each of an additional first language object and an additional second language object as proposed interpretations of the additional ambiguous input, the second language object and the additional second language object being the same; making a determination that at least one of a representation of the additional first input and a representation of the additional first output coincides with the contextual value; and responsive to said making a determination, outputting the at least a portion of the additional second language object at a position of preference with respect to the at least a portion of the additional first language object.
 8. The handheld electronic device of claim 7 wherein the operations further comprise: identifying as the additional first language object a word object representative of a relatively higher-frequency ambiguous word; identifying as the additional second language object a word object representative of a relatively lower-frequency ambiguous word; and making as said determination a determination that the lower-frequency ambiguous word, in the context of the additional first output, should be output at a position of preference to the higher-frequency ambiguous word.
 9. The handheld electronic device of claim 7 wherein the operations further comprise: detecting a selection of the at least a portion of the additional first language object; and responsive to said detecting a selection, deleting the contextual value.
 10. The handheld electronic device of claim 6 wherein the operations further comprise: identifying as the first language object a language object of a length equal to that of the ambiguous input; identifying as the second language object a language object of a length equal to that of the ambiguous input; identifying as the another first language object a language object of a length equal to that of the another ambiguous input; identifying as the another second language object a language object of a length equal to that of the another ambiguous input; and responsive thereto, initiating said storing and said associating. 